If we make some massive physics breakthrough tommrow is an LLM going to be able to fully integrate that into its current data set?
Or will we need to produce a host of documents and (re)train a new one in order for the concept to be deeply integrated.
This distinction is subtle but lost on many who think that our current path will get us to AGI...
That isn't to say we haven't created a meaningful tool but the sooner we get candid and realistic about what it is and how it works the sooner we can get down to the business of building practical applications with it. (And as an aside scaling it, something we arent doing well with now).
Why is retraining not allowed in this scenario? Yes, the model will know the breakthrough if you retrain. If you force the weights to stay static by fiat, then sure it's harder for them to learn, and will need go learn in-context or whatever. But that's true for you as well. If your brain is not allowed to update any connections I'm not sure how much you can learn either.
The reason that the models don't learn continuously is because it's currently prohibitively expensive. Imagine OpenAI retraining a model each time one of its 800m users sends a message. That'd make it aware instantly of every new development in the world or your life without any context engineering. There's a research gap here too but that'll be fixed with time and money.
But it's not a fundamental limitation of transformers as you make it out to be. To me it's just that things take time. The exact same architecture will be continuously learning in 2-3 years, and all the "This is the wrong path" people will need to shift goalposts. Note that I didn't argue for AGI, just that this isn't a fundamental limitiation.
What is the subtle distinction? I'm "many" and it's not clear at all here. If we had some massive physics breakthrough, the LLM needs to be tought about it, but so do people. Teaching people about it would involve producing a host of documents in some format but that's also true of teaching people. Training and learning here seem to be opposite ends of the same verb no matter the medium, but I'm open to being enlightened.
Not sure exactly what the parent comment intended, but it does seem to me that it's harder for an LLM to undergo a paradigm shift than for humans. If some new scientific result disproves something that's been stated in a whole bunch of papers, how does the model know that all those old papers are wrong? Do we withhold all those old papers in the next training run, or apply a super heavy weight somehow to the new one, or just throw them all in the hopper and hope for the best?
You approach it from a data-science perspective and ensure more signal in the direction of the new discovery. Eg saturating / fine-tuning with biased data in the new direction.
The "thinking" paradigm might also be a way of combatting this issue, ensuring the model is primed to say "wait a minute" - but this to me is cheating in a way, it's likely that it works because real thought is full of backtracking and recalling or "gut feelings" that something isn't entirely correct.
The models don't "know". They're just more likely to say one thing over another which is closer to recall of information.
These "databases" that talk back are an interesting illusion but the inconsistency is what you seem to be trying to nail here.
They have all the information encoded inside but don't layer that information logically and instead surface it based on "vibes".
Or will we need to produce a host of documents and (re)train a new one in order for the concept to be deeply integrated.
This distinction is subtle but lost on many who think that our current path will get us to AGI...
That isn't to say we haven't created a meaningful tool but the sooner we get candid and realistic about what it is and how it works the sooner we can get down to the business of building practical applications with it. (And as an aside scaling it, something we arent doing well with now).